Recent Research Progress of Superpixel Segmentation and Evaluation

Xuegang, 罗学刚 Luo; Junrui, 吕俊瑞 Lü; Zhenming, 彭真明 Peng

doi:10.3788/lop56.090005

Cited by 2 publications

(2 citation statements)

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“…Superpixel algorithm evaluation is an important part of superpixel research, and the common metrics for measuring algorithms now include edge accuracy and edge recall [8].…”

Section: Evaluation Of Image Segmentation Accuracymentioning

confidence: 99%

“…It is easy to cause problems such as over-segmentation, under-segmentation, and poor segmentation of the image, and cannot accurately segment the accurate edges of the target area. The superpixel segmentation method uses superpixels instead of pixels to represent features, which can reduce the complexity of image processing [8].The common superpixel segmentation methods include SEEDS segmentation [9], LSC segmentation [10] and SLIC segmentation [11]. Wang (2018) proposed an automatic extraction method of cultural relics diseases based on superpixel segmentation of orthographic images.…”

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confidence: 99%

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A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

Hu,

Huang,

XIA

et al. 2024

Preprint

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In recent years, with the development of three-dimensional digitization of cultural relics, most cultural relic protection units have a large number of fine three-dimensional data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface disease information from the fine three-dimensional color model of painted cultural relics and avoid the accuracy loss caused by reducing the dimension by conventional methods is an urgent problem to be solved in the investigation of cultural relics diseases.In view of the above problems, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on three-dimensional fine data. Firstly, this paper designs a two-dimensional and three-dimensional integrated data conversion model based on OSG three-dimensional engine, which realizes the mutual conversion between three-dimensional color model texture and two-dimensional image. Secondly, a SLIC segmentation algorithm with adaptive K value is proposed, which solves the problem of superpixel K value setting and improves the accuracy of image segmentation. Finally, through the two-dimensional and three-dimensional integrated model, the disease is statistically analyzed and labeled on the three-dimensional model.Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on three-dimensional fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive ; compared with the current three-dimensional manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring the extraction accuracy. The research method of this paper activates a large number of existing three-dimensional fine data of cultural protection units, and converts data mining and analysis from conventional two-dimensional data to three-dimensional data, which is more in line with the scientific utilization of data in accuracy and efficiency, and has certain scientific research value, leading value and practical significance.

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“…Superpixel algorithm evaluation is an important part of superpixel research, and the common metrics for measuring algorithms now include edge accuracy and edge recall [8].…”

Section: Evaluation Of Image Segmentation Accuracymentioning

confidence: 99%

mentioning

confidence: 99%

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

Hu,

Huang,

XIA

et al. 2024

Preprint

View full text Add to dashboard Cite

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Unsupervised Segmentation of Muscle Precursor Cell Images In Situ

2023

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In vitro culture of muscle stem cells on a large scale could bring light to the treatment of muscle-related diseases. However, the current work related to muscle stem cell culture is still only performed in specialized biological laboratories that are very much limited by manual experience. There are still some difficulties to achieve an automated culture of complex morphological cells in terms of live cell observation and morphological analysis. In this paper, a set of bright-field cell in situ imaging devices is designed to perform non-contact and invasive imaging of muscle precursor cells in vitro, and a neural network structured lightweight unsupervised semantic segmentation algorithm is proposed for the acquired images to achieve online extraction of cell regions of interest without manual annotation and pre-training. The algorithm first uses a graph-based super-pixel segmentation to obtain a coarse segmentation, then aggregates the coarse segmentation results with the help of Laplace operators as a reference to a four-layer convolutional neural network (CNN). The CNN parameters learn to refine the boundaries of the cells which helps the final segmentation accuracy and mean intersection–merge ratio reach 88% and 77%, respectively.

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Recent Research Progress of Superpixel Segmentation and Evaluation

Cited by 2 publications

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A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

Unsupervised Segmentation of Muscle Precursor Cell Images In Situ

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